Supervised low-light image and video enhancement has achieved remarkable progress, yet current models remain fundamentally constrained by the limitations of existing datasets. Prevailing methods typically rely on uncontrolled ambient captures, camera parameter manipulation (e.g., exposure or ISO adjustments), or neutral density filters, failing to replicate authentic night-sky near-infrared spectral content and genuine low-photon noise distributions. Furthermore, capturing dynamic paired sequences without introducing warping artifacts or sensor-specific biases remains an open challenge. In this paper, we introduce 3L-Dataset, a physically calibrated low-light image and video dataset generated using a modular, sensor-agnostic acquisition framework. Our test bench faithfully reproduces the standardized Midavaine night level scale from Level 1 (full moon, approximately 1000 mlx) down to Level 5 (overcast moonless night, approximately 0.1 mlx), spanning the visible to near-infrared spectrum across diverse realistic environments, including countryside, forest, urban, and desert scale models. Using high-precision programmable mechanical actuation, our acquisition framework guarantees sub-pixel spatial alignment across synchronized sextuplets, each composed of one reference image and five corresponding images acquired at the five standardized night levels, without post-processing image warping. We evaluate multiple representative deep learning baselines across varying night levels, revealing critical trade-offs between reconstruction fidelity, perceptual quality, and inference latency under severe photon starvation. The modular acquisition framework can be adapted to different cameras, optical systems, sensor technologies, and resolutions, enabling the dataset to be continuously expanded.
Figures & tables
Figure 1: Modelisation of our low-light dataset generation setup with : a) Light-proof enclosure, b) High-precision rotary stage, c) Acquisition system, d) External lightning system
Figure 2: Example of an aligned sextuplet acquired from the same scene at the same temporal instant. N0 corresponds to the reference image acquired under nominal illumination, while N1 to N5 correspond to the five standardized night levels.
Figure 3: Examples of scale models created by 3L-OPTRONICS to replicate real-world environments: a) Countryside, b) Forest, c) Desert, d) City.
Figure 4: Intrinsic dimension per illumination level on raw pixels, estimated with TwoNN and MLE ( k=5 ), from N0 (daylight reference) to N5 (darkest). Error bars give one standard deviation over 30 bootstrap samples; asterisks mark levels where the TwoNN estimate is unstable.
Figure 5: Content preservation per illumination level on raw pixels. Left: distribution of the Pearson correlation between each frame and its pixel-aligned daylight reference (N0). Right: effective signal-to-noise ratio derived from the median correlation, SNR=ρ2/(1−ρ2) ; the dashed line marks equal signal and noise power (0 dB).
Name
Light
Motion
Images/Videos
Resolution
Ground Truth
Alignment
BVI-LOWLIGHT [ 16 ]
Real
S
13,200
4240 × 2832
✓
✓
DUS (Static) [ 17 ]
Real
S
3,630
2160 × 1280
✓
✓
DUS (Dynamic) [ 17 ]
Real
D
15,000
2160 × 1280
×
×
DRV (Static) [ 18 ]
Real
S
20,847
5496 × 3672
✓
✓
DRV (Dynamic) [ 18 ]
Real
D
2,397
5496 × 3672
×
×
SMOID [ 5 ]
ND Filter
1D
35,800
1800 × 1000
✓
✓
Table 2: Comprehensive comparison of video and image datasets in low-light conditions. S and D denote static and dynamic scenes, respectively. 1D indicates a single type of motion. D* indicates stop motion .
Method
Year
Night level
PSNR (dB) ↑
SSIM ↑
LPIPS ↓
NIQE ↓
Inference time (ms) ↓
KinD [ 31 ]
2019
N3
41.98
0.9298
0.0277
16.01
87.22
N4
39.87
0.9232
0.0584
18.47
87.22
N5
38.41
0.9051
0.0766
19.96
87.22
Zero-DCE [ 30 ]
2020
N3
37.90
0.8693
0.7785
10.47
26.21
N4
37.71
0.8678
0.7769
10.57
26.17
N5
37.67
0.8671
0.7738
10.71
26.19
Table 3: Evaluation of representative low-light image enhancement networks trained on the proposed dataset at different night levels. The purpose of this benchmark is to assess the applicability of the dataset and the consistency of the results across illumination levels, rather than to rank the evaluated methods. Higher values are better for PSNR and SSIM, whereas lower values are better for LPIPS, NIQE, and inference time.
Low-light video processing remains an underexplored problem in computer vision, primarily due to the scarcity of high-quality ground-truth data. Existing public datasets exhibit several limitations, including restricted motion diversity, limited scene coverage, weak degradations, reliance on auxiliary modalities such as event data, and misalignment between low-light inputs and ground truth. Furthermore, current datasets typically address either low-light enhancement or scene understanding, whereas these tasks should be considered jointly. We introduce OLiVES, a benchmark dataset for outdoor low-light videos with dynamic scenes, designed to support both low-light video enhancement (LLVE) and video object segmentation (VOS). The dataset addresses key content gaps by incorporating a diverse range of natural textures, animals, occlusions, dynamic activities, and close-up scenes. To the best of our knowledge, OLiVES is the first outdoor low-light video dataset captured using professional cinema-grade cameras. It provides over 25,000 aligned normal- and low-light frame pairs, along with over 46,000 VOS annotations. We benchmark state-of-the-art LLVE and VOS methods on OLiVES and show that training on it improves performance on both tasks. The OLiVES project page is available at: https://anonymous-OLiVES.github.io/.
Yini Li, Joanne Lin, David Blackham +2
Visual Information Laboratory University of Bristol Bristol, United Kingdom
We introduce Dark3R, a framework for structure from motion in the dark that operates directly on raw images with signal-to-noise ratios (SNRs) below −4 dB -- a regime where conventional feature- and learning-based methods break down. Our key insight is to adapt large-scale 3D foundation models to extreme low-light conditions through a teacher--student distillation process, enabling robust feature matching and camera pose estimation in low light. Dark3R requires no 3D supervision; it is trained solely on noisy--clean raw image pairs, which can be either captured directly or synthesized using a simple Poisson--Gaussian noise model applied to well-exposed raw images. To train and evaluate our approach, we introduce a new, exposure-bracketed dataset that includes ∼42,000 multi-view raw images with ground-truth 3D annotations, and we demonstrate that Dark3R achieves state-of-the-art structure from motion in the low-SNR regime. Further, we demonstrate state-of-the-art novel view synthesis in the dark using Dark3R's predicted poses and a coarse-to-fine radiance field optimization procedure.
Andrew Y Guo, Anagh Malik, SaiKiran Tedla +7
University of Toronto · Vector Institute · York University +3
Existing deep learning-based low-light enhancement methods are typically trained on limited datasets with single enhancement targets, which restricts their generalization ability and controllability in real-world applications. To overcome these limitations, we propose ControlLight, a controllable, consistent, and generalizable framework for low-light enhancement. We first construct a large-scale dataset of real-world degraded images with continuous illumination-strength supervision. To further ensure consistent outputs under different control strengths, we introduce a misalignment-aware weighted flow matching loss that preserves image structure across continuous enhancement strengths. ControlLight allows users to edit real-world degraded low-light images toward satisfactory enhancement results by flexibly controlling the strength while preserving visual consistency and realism. Extensive experiments show that ControlLight achieves state-of-the-art performance against existing low-light enhancement approaches while demonstrating strong continuous controllability and generalization to real-world scenarios.
Yufeng Yang, Jianzhuang Liu, Jisheng Chu +4
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences · Zhejiang University